Digital assistant query intent recommendation generation

ABSTRACT

Systems and methods are provided for digital assistant configuration and functionality. For example, systems and methods provide for receiving a query from a user via a computing device, processing language in the query to identify a plurality of elements associated with the query, and analyzing the plurality of elements associated with the query to determine an intent of the query by mapping the plurality of elements associated with the query to a list of predetermined intents by comparing the plurality of elements associated with the query to each intent in the list of predetermined intents to generate a score for each intent in the list of predetermined intents. Systems and methods further provide for determining a subset of the predetermined intents based on the score for each intent in the list of predetermined intents, and providing recommendations related to the query based on the subset of predetermined intents.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/430,700, filed Dec. 6, 2016, entitled “CONFIGURATION OF AVOICE-ACTIVATED DIGITAL ASSISTANT,” which is incorporated herein byreference in its entirety.

BACKGROUND

An enterprise resource planning (ERP) system may provide integratedapplications and technology to automate many back office functionsrelated to technology, services, and human resources (for example). Forexample, ERP software may integrate various functions of an operation,such as product planning, development, manufacturing, sales andmarketing, in a database application and user interface. Accordingly, anERP system may have a functionally heavy interface against millions ofuser entries.

BRIEF DESCRIPTION OF THE DRAWINGS

Various ones of the appended drawings merely illustrate exampleembodiments of the present disclosure and should not be considered aslimiting its scope.

FIG. 1 is a block diagram illustrating a networked system, according tosome example embodiments.

FIG. 2 is a block diagram of an example system, according to someexample embodiments.

FIG. 3 is a block diagram illustrating example components of digitalassistant services, according to some example embodiments.

FIG. 4 is a block diagram illustrating example components of a naturallanguage processing (NLP) service, according to some exampleembodiments.

FIG. 5 is a flow chart illustrating aspects of a method, according tosome example embodiments, for generating recommendations related to aquery.

FIG. 6 illustrates an example user interface, according to some exampleembodiments.

FIG. 7 illustrates an example flow of a query being processed andtransformed to an oData or SQL request, according to some exampleembodiments.

FIG. 8 illustrates an example diagram for generating a score for eachintent in a list of predetermined intents, according to some exampleembodiments.

FIG. 9 is a flow chart illustrating aspects of a method, according tosome example embodiments, for generating a response to a query.

FIG. 10 is a flow chart illustrating aspects of a method, according tosome example embodiments, for mapping a query intent to a state diagramfor performing one or more operations associated with the query.

FIG. 11 illustrates an example state diagram, according to some exampleembodiments.

FIG. 12 is a flow chart illustrating aspects of a method, according tosome example embodiments, for switching between use case state diagrams.

FIG. 13 is a flow chart illustrating aspects of a method, according tosome example embodiments, for returning to a previously started use casestate diagram.

FIG. 14 is a diagram illustrating a uniform data structure, according tosome example embodiments.

FIG. 15 is a flow chart illustrating aspects of a method, according tosome example embodiments, for generating and providing data for aresponse to a query.

FIG. 16 is a block diagram illustrating an example of a softwarearchitecture that may be installed on a machine, according to someexample embodiments.

FIG. 17 illustrates a diagrammatic representation of a machine, in theform of a computer system, within which a set of instructions may beexecuted for causing the machine to perform any one or more of themethodologies discussed herein, according to an example embodiment.

DETAILED DESCRIPTION

Systems and methods described herein relate to configuration andfunctionality of a digital assistant. In one example, the digitalassistant is a voice activated digital assistant.

As explained above, an ERP system may have a functionally heavyinterface against millions of user entries. Embodiments described hereindescribe digital assistant functionality, such as recommendation andmachine learning, a decoupled framework that supports multiple datasources and transformation rules, state diagrams for managing dialog inthe digital assistant, and so forth.

Embodiments described herein may simplify user interactions in a naturalway and break down complexity in accessing information for data drivendecisions. Moreover, embodiments described herein provide for contextualinsights and intelligence processes. In example embodiments, the systemlearns from user interactions and contextual data to enable and definenew business processes and also help make applications proactive.

FIG. 1 is a block diagram illustrating a networked system 100, accordingto some example embodiments. The system 100 may include one or moreclient devices such as client device 110, also referred to herein as“computing device.” The client device 110 may comprise, but is notlimited to, a mobile phone, desktop computer, laptop, portable digitalassistant (PDA), smart phone, tablet, ultrabook, netbook, laptop,multi-processor system, microprocessor-based or programmable consumerelectronic, game console, set-top box, computer in a vehicle, or anyother communication device that a user may utilize to access thenetworked system 100. In some embodiments, the client device 110 maycomprise a display module (not shown) to display information (e.g., inthe form of user interfaces). In further embodiments, the client device110 may comprise one or more of touch screens, accelerometers,gyroscopes, cameras, microphones, global positioning system (GPS)devices, and so forth. The client device 110 may be a device of a userthat is used to request and receive information via a digital assistant,and so forth.

One or more users 106 may be a person, a machine, or other means ofinteracting with the client device 110. In example embodiments, the user106 may not be part of the system 100, but may interact with the system100 via the client device 110 or other means. For instance, the user 106may provide input (e.g., voice, touch screen input, alphanumeric input,etc.) to the client device 110 and the input may be communicated toother entities in the system 100 (e.g., third party servers 130, serversystem 102, etc.) via a network 104. In this instance, the otherentities in the system 100, in response to receiving the input from theuser 106, may communicate information to the client device 110 via thenetwork 104 to be presented to the user 106. In this way, the user 106may interact with the various entities in the system 100 using theclient device 110.

The system 100 may further include a network 104. One or more portionsof network 104 may be an ad hoc network, an intranet, an extranet, avirtual private network (VPN), a local area network (LAN), a wirelessLAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), ametropolitan area network (MAN), a portion of the Internet, a portion ofthe public switched telephone network (PSTN), a cellular telephonenetwork, a wireless network, a WiFi network, a WiMax network, anothertype of network, or a combination of two or more such networks.

The client device 110 may access the various data and applicationsprovided by other entities in the system 100 via web client 112 (e.g., abrowser, such as the Internet Explorer® browser developed by Microsoft®Corporation of Redmond, Wash. State) or one or more client applications114. The client device 110 may include one or more client applications114 (also referred to as “apps”) such as, but not limited to, a webbrowser, messaging application, electronic mail (email) application, ane-commerce site application, a mapping or location application, adigital assistant application, and the like.

In some embodiments, one or more client applications 114 may be includedin a given one of the client device 110, and configured to locallyprovide the user interface and at least some of the functionalities,with the client application 114 configured to communicate with otherentities in the system 100 (e.g., third party servers 130, server system102, etc.), on an as needed basis, for data and/or processingcapabilities not locally available (e.g., access ERP or customerrelationship management (CRM) data, to request data, to authenticate auser 106, to verify a method of payment, etc.). Conversely, one or moreapplications 114 may not be included in the client device 110, and thenthe client device 110 may use its web browser to access the one or moreapplications hosted on other entities in the system 100 (e.g., thirdparty servers 130, server system 102, etc.).

A server system 102 may provide server-side functionality via thenetwork 104 (e.g., the Internet or wide area network (WAN)) to one ormore third party servers 130 and/or one or more client devices 110. Theserver system 102 may include an application program interface (API)server 120, a web server 122, and digital assistant services server 124,that may be communicatively coupled with one or more databases 126.

The one or more databases 126 may be one or more storage devices thatstore data related to an ERP system, user data, and other data. The oneor more databases 126 may further store information related to thirdparty servers 130, third party applications 132, client devices 110,client applications 114, users 106, and so forth. The one or moredatabases 126 may include cloud-based storage in some embodiments.

The server system 102 may be a cloud computing environment, according tosome example embodiments. The server system 102, and any serversassociated with the server system 102, may be associated with acloud-based application, in one example embodiment.

The digital assistant services server 124 may provide back-end supportfor third party applications 132 and client applications 114, which mayinclude cloud-based applications. The digital assistant services server124 may receive queries, process queries, generate responses to queries,and so forth. Services provided by the server system 102 (e.g., via thedigital assistant services server 124) may be a RESTful service from auser question to a response to the question.

The system 100 may further include one or more third party servers 130.The one or more third party servers 130 may include one or more thirdparty application(s) 132. The one or more third party application(s)132, executing on third party server(s) 130, may interact with theserver system 102 via API server 120 via a programmatic interfaceprovided by the API server 120. For example, one or more the third partyapplications 132 may request and utilize information from the serversystem 102 via the API server 120 to support one or more features orfunctions on a website hosted by the third party or an applicationhosted by the third party. The third party website or application 132,for example, may provide digital assistant services functionality thatis supported by relevant functionality and data in the server system102.

FIG. 2 is a block diagram of an example system 200. The system 200comprises multichannel access 202 for a user 106, such as a browser,mobile apps, iMessage, short message service (SMS), email, and so forth(e.g., via client device 110). The system 200 further comprisesclient-side abstraction 204 such as speech to text, autocomplete, resulttemplates, conversation user interface (UI), interface for plug andplay, and so forth. The system 100 further comprises cloud platformservices 206 and digital assistant services 208. Digital assistantservices may comprise various components and services related to digitalassistant functionality. The system 200 may further comprise a businessabstraction layer 210 to interact with on premise systems 212, such asERP and on premise CRM via a cloud connector service 214. The businessabstraction layer may further interact with internet services 216, otheron premise systems, such as SAP Hana on premise 218, Vora tables/Graph220, and so forth.

FIG. 3 is a block diagram 300 illustrating additional details for thedigital assistant services 208. FIG. 4 is a block diagram 400illustrating additional details for the NLP service, comprising a nativeNLP 302 and third party NLP 304, and interaction with the digitalassistant services 208.

FIG. 5 is a flow chart illustrating aspects of a method 500, accordingto some example embodiments, for generating recommendations related to aquery. For illustrative purposes, method 500 is described with respectto the networked system 100 of FIG. 1. It is to be understood thatmethod 500 may be practiced with other system configurations in otherembodiments.

In operation 502, the server system 102 (e.g., via digital assistantservices server 124) receives a query created by a user 106. FIG. 6illustrates an example user interface 600 according to some embodiments.In the example in FIG. 6, a user 106 may input a query 602, such as“show alerts” via the user interface 600 on a client device 110 (e.g.,via voice input, text input, etc.). This query 602 is sent by the clientdevice 110 to the digital assistant services server 124 for processing.

The digital assistant services server 124 may have access to a knowledgebase (e.g., stored in one or more databases 126) that includes apredefined set of intents to be used to response to a query. In oneexample, the digital assistant services server 124 may not have modeleda query in the exact language used by the user 106, and thus, the queryis not included in the predefined set of intents in the knowledge base.For example, the server system 102 may have modeled queries such as“Show me my sales contact for company X” or “show me my contact forcompany X” but may not have modeled a query such as “sales” or “showalerts” or understand exactly what the user 106 may be requesting.Accordingly, the digital assistant services server 124 comprisesrecommendation services to respond to queries where the query is notclear or previously modeled. Moreover, the server system 102 may learnfrom the selection of recommendation by the user 106 so that next timethe user 106 inputs a similar query, it will be able to respondappropriately. The server system 102 may also unlearn from responsesfrom the user 106. In various examples, the server system 102 may use ascoring system, user or organization profiles, systems defaults, and soforth, to determine an intent associated with a query.

At both design time and run time, the different ways of identifying auser's intent by natural language may be done using technologies, suchas linguistic analysis, tokenization, Lemmatization, part-of-speech(POS) tagging, dependency parser, entity recognition, or other suchtechnologies.

In operation 504, the digital assistant services server 124 processeslanguage in the query to identify elements of the query. For example,the digital assistant services server 124 may process language in thequery to identify a plurality of elements associated with the query. Thedigital assistant services server 124 may use natural languageprocessing (NLP) technology such as a native NLP or third party NLP asshown in FIG. 4. FIG. 7 shows an example flow 700 of a query “Show me mytop 5 sales orders for this month” being processed and transformed to anoData or SQL Request.

In one example, an NLP input may include the user query, and an NLPoutput may comprise data associated with the query. In one example, thedata that is output from the NLP may be in the form of a JSON object. Inone example, the JSON object may comprise:

-   -   Original query    -   Returned query    -   Resource    -   Action    -   Context    -   EntityType    -   EntityValue    -   EntityPosition

In one example, the NLP input may be “show me my top 5 salesopportunities last quarter.” The NLP output may be a JSON objectcomprising the following information:

-   -   Original query: show me my top 5 sales opportunities last        quarter    -   Returned query: show me my top 5 sales opportunities from        2016-04-01 to 2016-06-30    -   EntityType: Integer    -   Entity Value: 5    -   EntityPosition: 1    -   EntityType: DATE    -   EntityValue: 2016-04-01    -   EntityPosition: 2    -   EntityValue: 2016-06-30    -   EntityType: DATE    -   EntityPosition: 3    -   Resource: opportunities    -   Action: show    -   Context: “ ”

In another example, the NLP input may be “show me current quarter top 5sales opportunities.” The NLP output may be a JSON object comprising thefollowing information:

-   -   Original query: show me current quarter top 3 sales        opportunities?    -   Returned query: show me top 3 sales opportunities from        2016-07-01 to 2016-09-30    -   EntityType: Integer    -   EntityValue: 3    -   EntityPosition: 1    -   EntityType: DATE    -   Entity Value: 2016-07-01    -   EntityPosition: 2    -   Entity Value: 2016-09-30    -   EntityType: DATE    -   EntityPosition: 3    -   Resource: opportunities    -   Action: show    -   Context: “ ”

Referring again to FIG. 5, in operation 506, the digital assistantservices server 124 analyzes the plurality of elements associated withthe query to determine an intent of the query. For example, the digitalassistant services server 124 may map the plurality of elementsassociated with the query to a list of predetermined intents bycomparing the plurality of elements to each intent in the list ofpredetermined intents to generate a score for each intent in the list ofpredetermined intents (e.g., based on how frequently each elementappears in each predetermined intent). The list of predetermined intentsmay be stored in one or more databases 126. In operation 508, thedigital assistant services server 124 generates a score for each intentin the list of predetermined intents.

FIG. 8 illustrates an example diagram 800 for generating a score foreach intent in the list of predetermined intents. The example diagram800 shows a list of intents 802, a number of questions 804 associatedwith each intent, a number of words 806 in the questions 804, a score810, a learned score 812, a penalty score 814, and a total score 816.Using the example query from above, “show alerts,” the digital assistantservices server 124 may compare each element (e.g., in this case using“show” and “alert” as elements) to the predetermined intents 802. Forexample, comparing the elements to the first intent may generate a scoreof 1 for the element “show” and a score of 9 for the element “alert” fora score 810 of 10. Then comparing the elements to the second intent maygenerate a score of 1 for show and 3 for sales for a score 810 of 4, andso forth. If the predetermined intent comprises a learned score 812 or apenalty score 814, those are also considered to determine a total score816. For example, a learned score 812 may be added to a score 810 and apenalty score 814 may be subtracted from a score 810.

In operation 510, the digital assistant services server 124 determines asubset of the predetermined intents based on the score for each of thepredetermined intents. In one example, the digital assistant servicesserver 124 may determine the subset of predetermined intents based onthe score for each intent by determining which of the predeterminedintents comprise a score that is above a predetermined threshold score.In another example, the digital assistant services server 124 maydetermine a subset of predetermined intents by ranking the list ofpredetermined intents by the score generated for each intent and thenselecting the top scoring predetermined intents. For example, thedigital assistant services server 124 may select the top two or threescored predetermined intents (e.g., to provide the most relevantrecommendations).

In operation 512, the digital assistant services server 124 providesrecommendations related to the query from the user 106 based on thesubset of predetermined intents. Using the example shown in FIG. 6, thedigital assistant services server 124 may provide a response 606 withthe recommendations “Show me my alerts or notifications,” “Do I have anyalerts or notifications,” and “Show my recent opportunity.”

FIG. 9 is a flow chart illustrating aspects of a method 900, accordingto some example embodiments, for generating a response to a query. Forillustrative purposes, method 900 is described with respect to thenetworked system 100 of FIG. 1. It is to be understood that method 900may be practiced with other system configurations in other embodiments.

In operation 902, the digital assistant services server 124 receives aselection of one intent of the subset of predetermined intents. Usingthe example in FIG. 6, the user 106 may select the recommendation “Showme my alerts or notifications.” The computing device 110 may send theselection to the digital assistant services server 124. The digitalassistant services server 124 may receive the selected intent andcalculate a learned score for the selected intent. The digital assistantservices server 124 may store the learned score for the selected intent.For example, the learned score may be associated with the selectedintent as shown in FIG. 8 (e.g., as learned score 20 for intent 1), andstored in one or more databases 126. The digital assistant servicesserver 124 may further calculate a total score 816 with the new learnedscore 812. The learned score may be associated with the user. In thisway the server system 102 may learn from the user. The next time theuser 106 inputs a similar query, the digital assistant services server124 may determine the intent based on the learned score and not have toprovide recommendations for the query again. For example, the digitalassistant services server 124 may receive a similar query (e.g., asecond query) from the user 106 via a computing device 110. As explainedabove, the digital assistant services server 124 may process thelanguage in the second query to identify a plurality of elementsassociated with the query. The digital assistant services server 124 maythen analyze the plurality of elements associated with the query todetermine an intent of the query by mapping the plurality of elementsassociated with the query to a list of predetermined intents bycomparing the plurality of elements associated with the query to eachintent in the list of predetermined intents to generate a score for eachintent in the list of predetermined intents. Since the second query isthe same or similar to the first query “show alerts” described above,the digital assistant services server 124 will select the predeterminedintent based on the learned score for the predetermined intent andgenerate a response to the second query, based on the selectedpredetermined intent.

The learned score may be associated with the user, an organizationassociated with the user, an entity associated with the user, theoverall system, or some combination of these or other entities. In oneexample, the server system 102 may analyze learned scores at a userlevel, organization level, or entity level, to determine if the learnedscore should be elevated to another level (e.g., based on statisticsassociated with the predetermined intent, learned score, etc.). Forexample, the server system 102 may determine that 90% of users in aparticular organization have a particular way of inputting a query. Theserver system 102 may automatically create a default intent for thisquery at the organizational level, or may provide a recommendation to anadministrator associated with the organization to do so.

For example, the server system 102 may determine that many usersassociated with a particular organization are phrasing a queryassociated with a particular intent in the same or similar way bydetermining that the particular intent is associated with a high learnedscore for a plurality of users in the organization. Accordingly, theserver system 102 may generate a learned score or default score for theparticular intent and associate the learned score with an organizationso that all the users may benefit from the knowledge. This may also bedone based on the learned scored at an organization level to affect alearned score at an entity level, at an entity level to affect a scoreat a system level, and so forth.

In operation 904, the digital assistant services server 124 retrievesdata related to the query based on the selection of intent of the subsetof predetermined intents. In one example, the data related to the querymay be retrieved by translating the query from the user 106 into a SQLstatement, oData request, or other means, to retrieve the data. In oneexample, data may be retrieved from a number of different data sourcesand the data sources may comprise different formats for the data. In oneexample, the digital assistant services server 124 may transform thedifferent formats of data from the different data sources into one dataformat.

In operation 906, the digital assistant services server 124 generatesthe response to the query and, in operation 908, the digital assistantservices server 124 sends the response to the computing device 110 andthe response is displayed to the user 106 (e.g., via a user interfacedisplayed on the computing device 110).

In addition to learning the intent of a user query, the server system102 may also “unlearn” an intent of a user query. For example, a user106 may select a recommendation incorrectly or change the meaning behinda query. The user 106 may indicate that a predetermined intent is nolonger accurate via a computing device 110 (e.g., via voice input, textinput, via a settings menu item, etc.). The digital assistant servicesserver 124 may receive the indication from the computing device that thepredetermined intent is not accurate for the intent of the query,calculate a penalty score for the predetermined intent, and store thepenalty score for the predetermined intent. For example, the digitalassistant server 124 may associate the penalty score for thepredetermined intent with the predetermined intent, which may be storedin one or more databases 126.

A penalty score may be associated with the user 106, an organizationassociated with the user 106, an entity associated with the user 106, asa system level penalty score, or some combination of these. Next timethe user 106 inputs the query associated with the predetermined intentwith the penalty score, the predetermined intent would not beautomatically chosen to generate a response to the query.

During processing of a query, the server system 102 may determine thatmore information is needed to generate a response to the query. Forexample, the user 106 may not have provided enough information andfurther information may not be able to be ascertained from theinformation provided. Example embodiments provide dialog functionalityto allow the server system 102 to get further information to generate aresponse to the query. For example, a user 106 may ask “Show me my salescontacts.” The server system 102 may need the company name to generate aresponse to the question. The server system 102 may check to see ifthere are any system defaults or defaults associated with a userprofile, organization profile, and so forth to see if it can determinethe company name. If the server system 102 cannot determine the companyname, it may ask the user 106 “For which company.”

In another example, there may be a confidence score for when the serversystem 102 assumes the intent of the answer. For example, if a user 106asks “what is the status of my order,” the server system 102 may have arelatively low confidence score because the query could be interpretedin several different ways (e.g., production order, purchase order,etc.). The confidence score may be used for internal use to determinewhether or not to ask follow up questions. For example, if a confidencescore is above a certain threshold, the server system 102 may justanswer the question based on the assumed intent and would not ask anyfollow up questions. If the confidence score is below a certainthreshold, the server system 102 may ask one or more follow up questionsto understand the intent.

In one example, the confidence score may also be used to give the user106 an indication of the confidence of the answer. For example, theanswer could appear to the user 106 in green for high confidence, yellowfor medium confidence, and red for low confidence.

The server system 102 may use the score for the intent described earlierand determine the confidence based on whether the score for the intentis over or under a certain threshold. There may be more than onethreshold. For example, there could be a threshold for a high confidencescore (e.g., an intent score greater than 25), a threshold range for amedium confidence score (e.g., an intent score between 10 and 25), and athreshold for a low confidence score (e.g., an intent score less than10).

In addition to a query requesting various data (e.g., productinformation, sales information, company name and location, etc.), a user106 may input a query that indicates that one or more operations beperformed. For example, a user 106 may be having trouble with aparticular product that he purchased. The user 106 may input a querythat indicates that he is having trouble with the particular product(e.g., a television, printer, power drill, mobile device, etc.) andneeds assistance. In one example, the server system 102 will create adialog to ask follow up questions to get more information from the user106 and also pull data from a knowledge source based on informationinput by the user 106. For example, the server system 102 may want torequest the product model number, ask if the user 106 would like toregister the product before creating a service ticket, and so forth. Theserver system 102 may ultimately create a service ticket to address thetrouble with the particular product.

In one example, the server system 102 may utilize a plurality of usecase state diagrams to create a dialog with the user 106 to getinformation to perform one or more operations associated with the query.Each use case state diagram may be associated with a particular use caseand may comprise a plurality of nodes to be traversed to collect dataand perform one or more operations. A simple example of a use case forcreating a service ticket is shown in FIG. 11 and described in furtherdetail below. The example use case state diagram in FIG. 11 comprises aplurality of nodes to generate data from the user or various sources.

In one example, each use case state diagram has one unique startingintent for the use case (e.g., a first or starting node in the statediagram). This may be a unique question with a unique intent identifier.In another example, each use case may have one or more ending intents(e.g., one or more ending nodes for the state diagram).

In one example, the data needed for each node may be static or hardcoded into the logic for each node. For example, if a user wants toregister a product, the specific data that the system needs to collectmay be predetermined. In one example, the data to be collected may be auser name and address, a user phone number and email, the date the userpurchased the product, the product name and model number, and so forth.

In another example, the data to be collected for a particular node maybe dynamic. For example, the logic for registering a product may be usedfor a plurality of entities, such as manufacturers of products, sellersof products, and so forth. The data to be collected for the dynamic nodemay be determined based on the product to be registered, the user, orother information. For example, the registration may be for a powerdrill from a first manufacturer. For registration, the firstmanufacturer may require the user contact information, productinformation, data of purchase, and a copy of the receipt. In anotherexample, the registration may be for headphones from a secondmanufacturer. For registration, the second manufacturer may require theuser contact information, product information, date of purchase, andmerchant name where the user purchased the product. The server system102 may have access to database tables or other storage structures thatcontain registration data for the first manufacturer and the secondmanufacturer. The server system 102 may access these tables to determinewhat information is contained in the table, and thus determine what datato request from the user or generate from other sources. The serversystem 102 may generate a data structure for the node containing thefields from the tables and then fill the fields with values from thedata generated. This allows for a more efficient system since the serversystem 102 will not have to hard code what data it needs to collect forevery single manufacturer, merchant, and so forth. This also allows fora more dynamic and flexible system since the server system 102 may nothave to update the logic and structure for each node each time an entitychanges its data, format of data, and so forth. Instead, the serversystem 102 may just determine the data needed from whatever is thelatest data structure for the entity.

In another example, the server system 102 may allow switching betweendifferent use case state diagrams. For example, the user 106 may switchto a second use case diagram without finishing the first use casediagram. In another example, the user 106 may switch back to the firstuse case diagram after he is finished with the second use case diagram,or without finishing the second use case diagram. For example, for eachquery the server systems determines if the intent of the query matchesan intent of a pending use case state diagram or a starting intent of anew use case state diagram that is not yet started. The values generatedfor each use case state diagram may be saved in memory until theparticular use case has ended so that a user 106 may switch back andforth between use cases without having to start over or provide the sameinformation again. Once the particular use case has ended, the generatedvalues may be removed automatically.

FIG. 10 is a flow chart illustrating aspects of a method 1000, accordingto some example embodiments, for mapping a query intent to a statediagram for performing one or more operations associated with the query.For illustrative purposes, method 1000 is described with respect to thenetworked system 100 of FIG. 1. It is to be understood that method 1000may be practiced with other system configurations in other embodiments.

In operation 1002, the server system 102 (e.g., via digital assistantservices server 124) receives a query input by a user 106 (e.g., via acomputing device 110 as described above). In operation 1004, the digitalassistant services server 124 processes the query to determine an intentof the query, as explained previously.

In operation 1006, the digital assistant services server 124 analyzesthe intent of the query against a plurality of use case state diagrams.In one example, there may be a plurality of use case state diagramsassociated with each of plurality of entities (e.g., companies ororganizations). For example, a user or entity may create 20-30 use casestate diagrams relevant to its business. In one example, the pluralityof use case state diagrams model various dialog flows for variouspredetermined use cases. Each of these use cases may be modeled based ona particular query and intent and have an associated predetermined statediagram that comprises a plurality of nodes to traverse to satisfy thequery. The predetermined state diagrams may be used to guide the user106 and collect the relevant data to complete one or more operations oractions associated with the query. The digital assistant services server124 may compare the intent of the query to each use case to determine ifthe intent is similar to a use case.

In one example, a node in the predetermined state diagram may be anintermediate node or a conditional node. An intermediate node may be anode that is associated with gathering particular data. For example, anintermediate node may be associated with a question “what is the modelnumber of your product.” The digital assistant services server 124 maydetermine the answer to this question based on data in prior nodes, datastored in one or more databases 126 internal or external to the serversystem 102, by requesting the data from the user 106, and so forth. Aconditional node may be associated with a question that can branch outto other nodes depending on the user's answer. For example, anintermediate node may be associated with the question “do you want toregister your product.” If the user 106 responds with a “yes,” then thestate diagram branches in one direction (e.g., to follow nodes forregistering the product); if the user 106 response with a “no,” then thestate diagram branches in another direction (e.g., to follow nodes toopen a service ticket).

In operation 1008, the digital assistant services server 124 determinesthat the intent of the query is associated with a first use case statediagram of the plurality of predetermined use case diagrams. Forexample, the digital assistant services server 124 may map the intent toa plurality of use case state diagrams to find the use case statediagram that is associated with the query. The first use case diagrammay comprise a plurality of nodes to traverse to satisfy the query. Eachnode may correspond to particular data that is needed to progressthrough the state diagram. Each node may comprise at least one key-valuepair. Data determined at each node is stored as a value associated witha key. For example, a node may correspond to the question “do you wantto register your product.” In one example, the key may be “productregistration” (e.g., the question or request for information associatedwith the node). The value may be “yes” or “no” depending on how the user106 responds. In one example, each node may comprise a unique identifierto identify the node.

In this way the digital assistant services server 124 collects data ateach node traversed. This data may be associated with a “context”whereby the data only persists in memory during the process oftraversing the use case state diagram. For example, the data may bestored in volatile memory (e.g., random-access memory (RAM)), instead ofpermanent storage (e.g., a database) and automatically discarded oncethe use case state diagram is completed or a user 106 quits or closes anapplication associated with the use case state diagram. This providesthe benefit of easier and faster access to data being collected, lessstorage space required in the system since the data is automaticallydiscarded after the use case is complete, and allows the system to reusedata that the user 106 has already provided so it does not ask redundantquestions of the user 106.

For each node in a flow of the use case state diagram, the digitalassistant services server 124 generates a value for the node, as shownin operation 1010. In one example, the digital assistant services server124 may determine whether data is available to determine the value forthe node. For example, the digital assistant services server 124 maylook for the value in previously generated values for nodes that werealready traversed for information already retrieved by the digitalassistant services server 124 or already provided by the user 106, orthe digital assistant services server 124 may access one or more sources(e.g., one or more databases 126) to generate the value. Based on adetermination that data is available to determine a value for the node,the digital assistant services server 124 may generate the value for thenode based on the data and store the value in the key-value pair for thenode.

In another example, the digital assistant services server 124 maydetermine that data is not available to determine a value for the node(e.g., from previously generate values) and may generate a requestmessage to request data from the user 106. The user 106 may provide datain a response to the request (e.g., via a computing device 110). Thedigital assistant services server 124 may receive the data from thecomputing device 110, and store the value in the key-value pair for thenode.

In operation 1012, the digital assistant services server 124 performsone or more operations associated with the query using the generatedvalues (e.g., the key-value pairs for the plurality of nodes thatcontain generated values). For example, the digital assistant servicesserver 124 may create a service ticket for an issue the user 106 ishaving with a particular product, based on the information ascertainedby the digital assistant services server 124 and/or provided by the user106 during traversal of the user case state diagram.

In operation 1014, the digital assistant services server 124 providesthe results of the operation. For example, the digital assistantservices server 124 may send the results to a computing device 110 to bedisplayed to a user 106. The generated values may be automaticallydeleted from memory after providing the results of the operation to theuser 106. In one example, the digital assistant services server 124 maydelete the generated values.

FIG. 11 illustrates an example state diagram 1100 for the use case tocreate a service ticket. In this example, a user 106 may input a query1102, such as “I have a problem with my TV.” There server system 102(e.g., via digital assistant services server 124) may then search for aproduct with the name “TV” at operation 1104. For example, the digitalassistant services server 124 may search one or more databases for thisinformation. If the search results in multiple results, the digitalassistant services server 124 may show the results to the user 106 toselect the correct product, at node 1106. The digital assistant servicesserver 124 may also, or in the alternative, ask the user 106 for furtherinformation, such as a product name, model number, type, and the like.The digital assistant services server 124 receives the product selectionor additional information from the user 106.

At node 1108, the digital assistant services server 124 determines ifthe product may be registered. If the product may be registered, thedigital assistant services server 124 determines whether the product isalready registered in node 1110. If the digital assistant servicesserver 124 determines that the product has not been registered, thedigital assistant services server 124 asks the user 106 if he would liketo register the product, in node 1112. If the digital assistant servicesserver 124 receives an indication that the user 106 would like toregister the product, in node 1114, the digital assistant servicesserver 124 posts a request to register the product in node 1116. Thedigital assistant services server 124 may then provide the productregistration details to the user 106, also at node 1116.

If the product has already been registered, if the user 106 does notwant to register the product, or after the user 106 has registered theproduct, the digital assistant services server 124 continues to node1118 to ask the user 106 if he would like to create a service ticket forthe product. If the user 106 indicates that he does not wish to create aservice ticket, the process ends at 1126. If the user 106 indicates thathe would like to create a service ticket, the digital assistant servicesserver 124 gathers information to create the service ticket at node1120. For example, digital assistant services server 124 may requestadditional information from the user 106, gather information fromproduct registration information, user data, and other sources, and soforth.

In node 1122, the digital assistant services server 124 posts a requestto create the service ticket. In node 1124, the digital assistantservices server 124 replies to the user 106 indicating that the serviceticket has been created and may provide the details of the serviceticket to the user 106. The process ends at 1126 and data for the usecase state diagram may be deleted from memory.

Example embodiments allow a user 106 to switch from one use case statediagram to another use case state diagram. FIG. 12 is a flow chartillustrating aspects of a method 1200, according to some exampleembodiments, for switching from a first use case state diagram to asecond use case state diagram, before completing the first use casestate diagram. For illustrative purposes, method 1200 is described withrespect to the networked system 100 of FIG. 1. It is to be understoodthat method 1200 may be practiced with other system configurations inother embodiments.

In operation 1202, the digital assistant services server 124 determinesthat response data received from the user 106 in response to a requestmessage requesting data, or generally input by the user 106, is notrelevant to the request in the request message, or not relevant to thecontext of the use case state diagram. For example, the digitalassistant services server 124 may be requesting information aboutregistering a product from a user 106 and the user 106 may input “showme my vacation balance.” Example embodiments allow a user 106 to switchbetween use cases and to come back from where the user 106 left off in aprevious use case. In one example, the values generated in the first usecase state diagram, before the user 106 switches, is still kept inmemory (e.g., RAM) in case the user 106 returns to the state diagram.

The remainder of the flow chart in FIG. 12, related to response datareceived from a user 106, illustrates a similar process as the flowchart in FIG. 10 which is related to a query received from a user 106.Accordingly, the details included in the description of FIG. 10 apply tothe flow illustrated in FIG. 12 and may not be repeated here.

In operation 1204, the digital assistant services server 124 processesthe response data to determine an intent of the response data, asexplained previously for determining an intent of a query. In operation1206, the digital assistant services server 124 analyzes the intent ofthe response data against the plurality of predetermined use case statediagrams, and in operation 1208, the digital assistant services server124 determines that the intent of the response data is associated with asecond use case state diagram of the plurality of use case statediagrams, as also explained previously.

For each node in a flow of the use case state diagram, the digitalassistant services server 124 generates a value for the node, as shownin operation 1210. In one example, the digital assistant services server124 may determine whether data is available to determine the value forthe node. For example, the digital assistant services server 124 maylook for the value in previously generated values for nodes that werealready traversed for information already retrieved by the digitalassistant services server 124, already provided by the user 106, oravailable in one or more databases 126 or other sources. Based on adetermination that data is available to determine a value for the node,the digital assistant services server 124 may generate the value for thenode based on the data and store the value in the key-value pair for thenode.

In another example, the digital assistant services server 124 maydetermine that data is not available to determine a value for the node(e.g., from previously generated values or other sources) and maygenerate a request message to request data from the user 106. The user106 may provide data in a response to the request (e.g., via a computingdevice 110). The digital assistant services server 124 may receive thedata from the computing device 110, and store the value in the key-valuepair for the node.

In operation 1212, the digital assistant services server 124 performsone or more operations associated with the query, using the generatedvalues (e.g., the key-value pairs for the plurality of nodes thatcontain generated values). For example, the digital assistant servicesserver 124 may create a service ticket for an issue the user 106 ishaving with a particular product, based on the information ascertainedby the digital assistant services server 124 and/or provided by the user106, during traversal of the user case state diagram.

In operation 1214, the digital assistant services server 124 providesthe results of the operation. For example, the digital assistantservices server 124 may send the results to a computing device 110 to bedisplayed to a user 106. The generated values may be automaticallydeleted from memory after providing the results of the operation to theuser 106.

Example embodiments allow a user 106 to switch from one use case statediagram to another use case state diagram, and then return to a previoususe case state diagram. FIG. 13 is a flow chart illustrating aspects ofa method 1300, according to some example embodiments, for returning to apreviously started use case state diagram. For illustrative purposes,method 1300 is described with respect to the networked system 100 ofFIG. 1. It is to be understood that method 1300 may be practiced withother system configurations in other embodiments.

In operation 1302, the digital assistant services server 124 receives aquery (e.g., a new query or second query). Or, the digital assistantservices server 124 may determine that response data received from theuser 106 in response to a request message requesting data, or generallyinput by the user 106, is not relevant to the request in the requestmessage, or not relevant to the context of the use case state diagram.For example, the digital assistant services server 124 may be requestinginformation about a vacation balance from a user 106 and the user 106may input “I want to continue with product registration.” Exampleembodiments allow a user 106 to switch between use cases and to comeback from where the user 106 left of in a previous use case.

The operations in the flow chart in FIG. 13 follow a similar process asthe flow chart in FIG. 10. Accordingly, the details included in thedescription of FIG. 10 may apply to the flow illustrated in FIG. 13 butmay not be repeated here.

In operation 1304, the digital assistant services server 124 processesthe query to determine an intent of the query, as explained previouslyfor determining an intent of a query. In operation 1306, the digitalassistant services server 124 determines that the intent of the query isassociated with a previously started use case state diagram. Asexplained above, the generated values for a plurality of nodes of thepreviously started use case diagram may continue to persist in memoryuntil the use case state diagram is completed. The digital assistantservices server 124 may first compare the intent of the query to anypreviously started use case diagrams to determine whether it is relevantto any of the previously started use case diagrams before comparing theintent of the query against the plurality of predetermined use casestate diagrams.

In operation 1308, the digital assistant services server 124 determinesa node where the user 106 left off in the previously started use casestate diagram. In one example, the digital assistant services server 124may determine the node where the user 106 left off by determining whichnode is the last node in a flow of nodes with generated values.

Continuing from the node where the user 106 left off, for each node in aflow of the use case state diagram, the digital assistant servicesserver 124 generates a value for the node, as shown in operation 1310.In one example, the digital assistant services server 124 may determinewhether data is available to determine the value for the node. Forexample, the digital assistant services server 124 may look for thevalue in previous generated values for nodes that were already traversedfor information already retrieved by the digital assistant servicesserver 124 or already provided by the user 106. Based on a determinationthat data is available to determine a value for the node, the digitalassistant services server 124 may generate the value for the node basedon the data and store the value in the key-value pair for the node.

In another example, the digital assistant services server 124 maydetermine that data is not available to determine a value for the node(e.g., from previously generated values) and may generate a requestmessage to request data from the user 106. The user 106 may provide datain a response to the request (e.g., via a computing device 110). Thedigital assistant services server 124 may receive the data from thecomputing device 110 and store the value in the key-value pair for thenode.

In operation 1312, the digital assistant services server 124 performsone or more operations associated with the query using the generatedvalues (e.g., the key-value pairs for the plurality of nodes thatcontain generated values). For example, the digital assistant servicesserver 124 may create a service ticket for an issue the user 106 ishaving with a particular product, based on the information ascertainedby the digital assistant services server 124 and/or provided by the user106, during traversal of the user 106 case state diagram.

In operation 1314, the digital assistant services server 124 providesthe results of the operation. For example, the digital assistantservices server 124 may send the results to a computing device 110 to bedisplayed to a user 106. The digital assistant services server 124 maydelete the generated values from memory after providing the results ofthe operation to the user 106.

Digital assistant services may comprise logging services to log the user106 dialog (e.g., user questions and responses) for sentiment analysisand machine learning. In one example, all of the dialog may be logged,and it may be queried for analytics and also serve as a machine learningtool for the system to give recommendation on the questions that do notmatch configured services. From the logged data, analytics may beperformed to provide reporting and analysis for sentiment analysis,usage reports, and so forth.

As explained above, there may be many data sources for data used togenerate a response to a query. These data sources may originate from anumber of separate entities which may all comprise a number of differentstructures and formats for the data. There may be different structuresand formats in each data source as well as across different datasources. For example, each entity may have different systems, such asERP, human capital management (HCM), CRM, and other systems and they mayhave data in different formats such as in database tables, in a filesystem, as a service on the Internet, in a tree structure, as plain textor rich text, and so forth. One technical challenge with having manydata sources with many different structures and formats for the data inthe data sources, is how to provide and generate responses to queries toa variety of different channels and interfaces. For example, a user 106may access digital assistant services using a client device 110 (e.g.,personal computer, mobile phone, tablet, etc.) via one or more channels(also referent to herein as “interface type”), such as a web browser, amobile app, an application, a chat bot (e.g., Slack, Facebook messenger,etc.), via SMS, via email, via MMS, and the like.

One way to address the challenge of supporting multiple channel accessto the digital assistant services may be to develop a separateimplementation for each possible channel or user interface on eachaccess channel. For example, a user interface layout may be designed foreach separate user interface that maps to each type of data in each typeof data source. This may take considerable development time andresources to support so many different channels. Moreover, data fromdifferent data sources may be needed to generate one or more responses.

Another way to address the challenge of supporting multiple channelaccess to the digital assistant services may be to format all of thedata for all of the data sources in the exact same format and structure.Given the number of different entities and the vast amount of dataavailable for each entity and data source, this is likely veryimpractical if not impossible.

Example embodiments described herein enable different channels to accessthe same set of data to provide responses to queries, regardless of thedata source or format of the data. In one example, example embodimentsprovide an output to be able to display on all different channelsalready used by a user 106. Example embodiments may provide a service tobe consumed by existing and new channels whereby the data sources aredecoupled from the user interface (UI) for the channels.

In one example, the data from different data sources may be transformedinto a single uniform data structure during runtime of the queryresponse generation process. The single uniform data structure may be apredefined data structure or a protocol for a user interface (UI)associated with a channel to consume. In this way the single uniformdata structure can be a service that may be used by any UI associatedwith a channel. The single uniform data structure may be a flexible datastructure that can handle many formats of data. For example, the datafrom one or more data source may be in a list format, a map format, asingle field, and embedded or tree structure, an XML format, and soforth. Using a single uniform data structure may allow for minimaldevelopment to support each type of interface associated with multiplechannels to access the digital assistant services.

FIG. 14 illustrates an example of a single uniform data structure 1400,according to some example embodiments. The single uniform data structure1400 may comprise various elements. For example, a values element 1402may comprise a number of objects that are populated to include theactual data from one or more data sources. Each object may comprise anumber of fields, such as name, subtitle, description, location, type,and the like. These fields may be populated with data from one or moredata sources. In this example, the name field is populated with “CurrentLocation,” the subtitle field is populated with “El Camino Street,” thedescription field is left blank, the location field is populated with alongitude and latitude of a particular location, and the type field ispopulated with “CurrentLocation.”

The single uniform data structure 1400 may further comprise a UI title1404 with a text field and an icon field. In this example the text fieldmay be populated with “parking available” and the icon field may bepopulated with “maps.” A query field 1412 may be populated with thequery that was created by the user. In this example the query field ispopulated with “show me available parking nearby.”

A UI type element 1406 may indicate a template type. Example embodimentsmay include predefined templates to instruct the UI how to render theparticular data. In one example, a finite number of predefined templatesmay be used (e.g., 7, 10, etc.) to further simplify the implementationprocess and use of the service. For example there may be a template fordisplaying map data, a template for displaying a table or list, atemplate for displaying a table select (e.g., to display data to a userthat may be selected by a user (e.g., include an interactive link) toget more information on various data displayed), a speech template forresponding to the question in voice speech, a notification template thatallows for push notifications (e.g., when a user first starts up theinterface, etc.), and so forth. Each of the predefined templates mayhave the same single uniform data structure, no matter whether thetemplate is for a map, a table, natural language speech, and so forth.In this way a uniform protocol between multiple channels to the serviceis established. Moreover this makes it easy for a UI to adapt to theservice because of the simple uniform data structure and finite set ofresults templates.

The single uniform data structure 1400 may further comprise a UIConfigelement 1408. This element may comprise metadata describing the datavalues for each object in the values element 1402. For example themetadata may describe each field, a field type for each field (e.g.,text, integer, amount, currency, location), how to render the data, ahierarchy or sequence for rendering the data, and so forth. The metadatamay be used by the UI to determine the type of data and how to displaythe data.

The single uniform data structure 1400 may further comprise a speechresponse 1410. In this example the speech response may be “Looking foravailable parking.”

In one example, transformation rules may be used to define how to mapdata from one or more data sources to the uniform data structure. Forexample, the transformation rules may comprise instructions fortranslating the data from at least one data source to response data inthe uniform data structure. In one example, transformation rules may beset up for each data source. There may be some predefined transformationrules to allow an entity or the server system 102 to designtransformation rules for one or more data sources. Some examples oftransformation rules may be to select certain fields or hide certainfields in a data source, add a field, change one or more field names,calculate or merge fields (e.g., amount and currency in one single fieldto display, or location longitude and latitude put in single field,etc.), map one field to another, and so forth. Transformation rules maybe developed at design time so that the server system 102 may use themat runtime to transform data to the single uniform data structure to beconsumed by one or more channels.

FIG. 15 is a flow chart illustrating aspects of a method 1500, accordingto some example embodiments, for generating and providing data for aresponse to a query. For illustrative purposes, method 1500 is describedwith respect to the networked system 100 of FIG. 1. It is to beunderstood that method 1500 may be practiced with other systemconfigurations in other embodiments.

In operation 1502, the server system 102 (e.g., via digital assistantservices server 124) receives a plurality of queries from a plurality ofchannels. For example the digital assistant services server 124 mayreceive a plurality of queries from a plurality of channels via aplurality of computing devices 110. Each channel of the plurality ofchannels may comprise one of a plurality of different interface types,as explained above.

The digital assistant services server 124 processes each query. Inoperation 1504, the digital assistant services server 124 determines ause case associated with each query. For example, the digital assistantservices server 124 may process the query to determine an intent of thequery and determines a use case associated with the intent of the queryfrom a plurality of use cases, as described in further detail above.

In operation 1506, the digital assistant services server 124 determinestransformation rules for data associated with the use case. For example,transformation rules may be stored in one or more databases 126 andassociated with the plurality of use cases. The digital assistantservices server 124 may access the one or more databases 126 todetermine what transformation rules are associated with the use case forthe query.

In operation 1508, the digital assistant services server 124 may accessdata from at least one data source of a plurality of data sources togenerate response data for a response to the query. The plurality ofdata sources may comprise data in a plurality of different formats, asexplained above.

In operation 1510, the digital assistant services server 124 transformsthe data associated with the use case from at least a first format intoa uniform data structure comprising the response data using thetransformation rules. For example the digital assistant services server124 may use the transformation rules to change one or more field namesin the data, merge one or more fields in the data, calculate values forcertain fields based on multiple fields in the data, and so forth.

In operation 1512, the digital assistant services server 124 providesthe response data in the uniform data structure to the computing deviceto be rendered in a user interface. For example, the digital assistantservices server 124 sends the response data in the uniform datastructure to the computing device. The computing device may then renderthe response data in a user interface for a user 106. To render theresponse data, the computing device may loop through each element in themetadata associated with each value of the response data to display theresponse data according to the metadata.

The following examples describe various embodiments of methods,machine-readable media, and systems (e.g., machines, devices, or otherapparatus) discussed herein.

Example 1

A method comprising:

receiving, at a server computer, a query from a user via a computingdevice;

processing, by the server computer, language in the query to identify aplurality of elements associated with the query;

analyzing, by the server computer, the plurality of elements associatedwith the query to determine an intent of the query by mapping theplurality of elements associated with the query to a list ofpredetermined intents by comparing the plurality of elements associatedwith the query to each intent in the list of predetermined intents togenerate a score for each intent in the list of predetermined intents;

determining a subset of the predetermined intents based on the score foreach intent in the list of predetermined intents; and

providing, to the computing device, recommendations related to the queryfrom the user based on the subset of predetermined intents.

Example 2

A method according to Example 1, further comprising:

ranking the list of predetermined intents by the score generated foreach intent.

Example 3

A method according to any of the previous examples, wherein determiningthe subset of the predetermined intents is based on a score for eachintent in the subset of predetermined intents that is above apredetermined threshold score.

Example 4

A method according to any of the previous examples, further comprising:

receiving a selection of one intent of the subset of predeterminedintents;

calculating a learned score for the one intent; and

storing the calculated learned score for the one intent.

Example 5

A method according to any of the previous examples, wherein the query isa first query, and wherein the method further comprises:

receiving a second query from a user via a computing device;

processing language in the second query to identify a plurality ofelements associated with the second query;

analyzing the plurality of elements associated with the query todetermine an intent of the query by mapping the plurality of elementsassociated with the query to a list of predetermined intents bycomparing the plurality of elements associated with the query to eachintent in the list of predetermined intents to generate a score for eachintent in the list of predetermined intents;

selecting a predetermined intent based on the learned score for thepredetermined intent;

generating a response to the second query, based on the selectedpredetermined intent.

Example 6

A method according to any of the previous examples, further comprising:

determining that a particular intent is associated with a high learnedscore for a plurality of users in a single organization;

generating a learned score for the intent and associating it with thesingle organization.

Example 7

A method according to any of the previous examples, further comprising:

determining that a particular intent is associated with a high learnedscore for a plurality of users across multiple organizations in asystem;

creating a default score for the intent in the system.

Example 8

A method according to any of the previous examples, further comprising:

receiving an indication from the computing device that at least one ofthe subset of predetermined intents is not accurate for the intent ofthe query;

calculating a penalty score for the at least one of the subset ofpredetermined intents; and

storing the penalty score for the one intent and associating it with theuser.

Example 9

A method according to any of the previous examples, further comprising:

receiving a selection of one intent of the subset of predeterminedintents;

retrieving data related to the query based on the selection of oneintent of the subset of predetermined intents;

generating a response based on the retrieved data; and

sending the response to the query to the computing device, wherein theresponse to the query is displayed to a user.

Example 10

A method according to any of the previous examples, wherein the dataretrieved related to the query comprises different formats of data fromdifferent data sources, and wherein transforming the data into a formatfor a response to the query comprises transforming the different formatsof data from the different data sources into one data format.

Example 11

A server computer comprising:

at least one processor; and

a computer-readable medium coupled with the at least one processor, thecomputer-readable medium comprising instructions stored thereon that areexecutable by the at least one processor to cause the server computer toperform operations comprising:

-   -   receiving a query from a user via a computing device;    -   processing language in the query to identify a plurality of        elements associated with the query;    -   analyzing the plurality of elements associated with the query to        determine an intent of the query by mapping the plurality of        elements associated with the query to a list of predetermined        intents by comparing the plurality of elements associated with        the query to each intent in the list of predetermined intents to        generate a score for each intent in the list of predetermined        intents;    -   determining a subset of the predetermined intents based on the        score for each intent in the list of predetermined intents; and    -   providing, to the computing device, recommendations related to        the query from the user based on the subset of predetermined        intents.

Example 12

A server computer according to any of the previous examples, whereindetermining the subset of the predetermined intents is based on a scorefor each intent in the subset of predetermined intents that is above apredetermined threshold score.

Example 13

A server computer according to any of the previous examples, theoperations further comprising:

receiving a selection of one intent of the subset of predeterminedintents;

calculating a learned score for the one intent; and

storing the calculated learned score for the one intent.

Example 14

A server computer according to any of the previous examples, wherein thequery is a first query, and wherein the operations further comprises:

receiving a second query from a user via a computing device;

processing language in the second query to identify a plurality ofelements associated with the second query;

analyzing the plurality of elements associated with the query todetermine an intent of the query by mapping the plurality of elementsassociated with the query to a list of predetermined intents bycomparing the plurality of elements associated with the query to eachintent in the list of predetermined intents to generate a score for eachintent in the list of predetermined intents;

selecting a predetermined intent based on the learned score for thepredetermined intent;

generating a response to the second query, based on the selectedpredetermined intent.

Example 15

A server computer according to any of the previous examples, theoperations further comprising:

determining that a particular intent is associated with a high learnedscore for a plurality of users in a single organization;

generating a learned score for the intent and associating it with thesingle organization.

Example 16

A server computer according to any of the previous examples, theoperations further comprising:

determining that a particular intent is associated with a high learnedscore for a plurality of users across multiple organizations in asystem;

creating a default score for the intent in the system.

Example 17

A server computer according to any of the previous examples, theoperations further comprising:

receiving an indication from the computing device that at least one ofthe subset of predetermined intents is not accurate for the intent ofthe query;

calculating a penalty score for the at least one of the subset ofpredetermined intents; and

storing the penalty score for the one intent and associating it with theuser.

Example 18

A server computer according to any of the previous examples, theoperations further comprising:

receiving a selection of one intent of the subset of predeterminedintents;

retrieving data related to the query based on the selection of oneintent of the subset of predetermined intents;

generating a response based on the retrieved data; and

sending the response to the query to the computing device, wherein theresponse to the query is displayed to a user.

Example 19

A server computer according to any of the previous examples, wherein thedata retrieved related to the query comprises different formats of datafrom different data sources, and wherein transforming the data into aformat for a response to the query comprises transforming the differentformats of data from the different data sources into one data format.

Example 20

A non-transitory computer-readable medium comprising instructions storedthereon that are executable by at least one processor to cause acomputing device to perform operations comprising:

receiving a query from a user via a computing device;

processing language in the query to identify a plurality of elementsassociated with the query;

analyzing the plurality of elements associated with the query todetermine an intent of the query by mapping the plurality of elementsassociated with the query to a list of predetermined intents bycomparing the plurality of elements associated with the query to eachintent in the list of predetermined intents to generate a score for eachintent in the list of predetermined intents;

determining a subset of the predetermined intents based on the score foreach intent in the list of predetermined intents; and

providing, to the computing device, recommendations related to the queryfrom the user based on the subset of predetermined intents.

FIG. 16 is a block diagram 1600 illustrating software architecture 1602,which can be installed on any one or more of the devices describedabove. For example, in various embodiments, client devices 110 andserver systems 130, 102, 120, 122, and 124 may be implemented using someor all of the elements of software architecture 1602. FIG. 16 is merelya non-limiting example of a software architecture, and it will beappreciated that many other architectures can be implemented tofacilitate the functionality described herein. In various embodiments,the software architecture 1602 is implemented by hardware such asmachine 1700 of FIG. 17 that includes processors 1710, memory 1730, andI/O components 1750. In this example, the software architecture 1602 canbe conceptualized as a stack of layers where each layer may provide aparticular functionality. For example, the software architecture 1602includes layers such as an operating system 1604, libraries 1606,frameworks 1608, and applications 1610. Operationally, the applications1610 invoke application programming interface (API) calls 1612 throughthe software stack and receive messages 1614 in response to the APIcalls 1612, consistent with some embodiments.

In various implementations, the operating system 1604 manages hardwareresources and provides common services. The operating system 1604includes, for example, a kernel 1620, services 1622, and drivers 1624.The kernel 1620 acts as an abstraction layer between the hardware andthe other software layers, consistent with some embodiments. Forexample, the kernel 1620 provides memory management, processormanagement (e.g., scheduling), component management, networking, andsecurity settings, among other functionality. The services 1622 canprovide other common services for the other software layers. The drivers1624 are responsible for controlling or interfacing with the underlyinghardware, according to some embodiments. For instance, the drivers 1624can include display drivers, camera drivers, BLUETOOTH® or BLUETOOTH®Low Energy drivers, flash memory drivers, serial communication drivers(e.g., Universal Serial Bus (USB) drivers), WI-FI® drivers, audiodrivers, power management drivers, and so forth.

In some embodiments, the libraries 1606 provide a low-level commoninfrastructure utilized by the applications 1610. The libraries 1606 caninclude system libraries 1630 (e.g., C standard library) that canprovide functions such as memory allocation functions, stringmanipulation functions, mathematic functions, and the like. In addition,the libraries 1606 can include API libraries 1632 such as medialibraries (e.g., libraries to support presentation and manipulation ofvarious media formats such as Moving Picture Experts Group-4 (MPEG4),Advanced Video Coding (H.264 or AVC), Moving Picture Experts GroupLayer-3 (MP3), Advanced Audio Coding (AAC), Adaptive Multi-Rate (AMR)audio codec, Joint Photographic Experts Group (JPEG or JPG), or PortableNetwork Graphics (PNG)), graphics libraries (e.g., an OpenGL frameworkused to render in two dimensions (2D) and in three dimensions (3D)graphic content on a display), database libraries (e.g., SQLite toprovide various relational database functions), web libraries (e.g.,WebKit to provide web browsing functionality), and the like. Thelibraries 1606 can also include a wide variety of other libraries 1634to provide many other APIs to the applications 1610.

The frameworks 1608 provide a high-level common infrastructure that canbe utilized by the applications 1610, according to some embodiments. Forexample, the frameworks 1608 provide various graphic user interface(GUI) functions, high-level resource management, high-level locationservices, and so forth. The frameworks 1608 can provide a broad spectrumof other APIs that can be utilized by the applications 1610, some ofwhich may be specific to a particular operating system 1604 or platform.

In an example embodiment, the applications 1610 include a homeapplication 1650, a contacts application 1652, a browser application1654, a book reader application 1656, a location application 1658, amedia application 1660, a messaging application 1662, a game application1664, and a broad assortment of other applications such as a third partyapplications 1666. According to some embodiments, the applications 1610are programs that execute functions defined in the programs. Variousprogramming languages can be employed to create one or more of theapplications 1610, structured in a variety of manners, such asobject-oriented programming languages (e.g., Objective-C, Java, or C++)or procedural programming languages (e.g., C or assembly language). In aspecific example, the third party application 1666 (e.g., an applicationdeveloped using the ANDROID™ or IOS™ software development kit (SDK) byan entity other than the vendor of the particular platform) may bemobile software running on a mobile operating system such as IOS™,ANDROID™, WINDOWS® Phone, or another mobile operating system. In thisexample, the third party application 1666 can invoke the API calls 1612provided by the operating system 1604 to facilitate functionalitydescribed herein.

Some embodiments may particularly include a digital assistantapplication 1667. In certain embodiments, this may be a stand-aloneapplication that operates to manage communications with a server systemsuch as third party servers 130 or server system 102. In otherembodiments, this functionality may be integrated with anotherapplication. The digital assistant application 1667 may request anddisplay various data related to ERP or other systems and may provide thecapability for a user 106 to input data related to the system via voice,a touch interface, a keyboard, or using a camera device of machine 1700,communication with a server system via I/O components 1750, and receiptand storage of object data in memory 1730. Presentation of informationand user inputs associated with the information may be managed bydigital assistant application 1667 using different frameworks 1608,library 1606 elements, or operating system 1604 elements operating on amachine 1700.

FIG. 17 is a block diagram illustrating components of a machine 1700,according to some embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.Specifically, FIG. 17 shows a diagrammatic representation of the machine1700 in the example form of a computer system, within which instructions1716 (e.g., software, a program, an application 1610, an applet, an app,or other executable code) for causing the machine 1700 to perform anyone or more of the methodologies discussed herein can be executed. Inalternative embodiments, the machine 1700 operates as a standalonedevice or can be coupled (e.g., networked) to other machines. In anetworked deployment, the machine 1700 may operate in the capacity of aserver machine 130, 102, 120, 122, 124, etc., or a client device 110 ina server-client network environment, or as a peer machine in apeer-to-peer (or distributed) network environment. The machine 1700 cancomprise, but not be limited to, a server computer, a client computer, apersonal computer (PC), a tablet computer, a laptop computer, a netbook,a personal digital assistant (PDA), an entertainment media system, acellular telephone, a smart phone, a mobile device, a wearable device(e.g., a smart watch), a smart home device (e.g., a smart appliance),other smart devices, a web appliance, a network router, a networkswitch, a network bridge, or any machine capable of executing theinstructions 1716, sequentially or otherwise, that specify actions to betaken by the machine 1700. Further, while only a single machine 1700 isillustrated, the term “machine” shall also be taken to include acollection of machines 1700 that individually or jointly execute theinstructions 1716 to perform any one or more of the methodologiesdiscussed herein.

In various embodiments, the machine 1700 comprises processors 1710,memory 1730, and I/O components 1750, which can be configured tocommunicate with each other via a bus 1702. In an example embodiment,the processors 1710 (e.g., a central processing unit (CPU), a reducedinstruction set computing (RISC) processor, a complex instruction setcomputing (CISC) processor, a graphics processing unit (GPU), a digitalsignal processor (DSP), an application specific integrated circuit(ASIC), a radio-frequency integrated circuit (RFIC), another processor,or any suitable combination thereof) include, for example, a processor1712 and a processor 1714 that may execute the instructions 1716. Theterm “processor” is intended to include multi-core processors 1710 thatmay comprise two or more independent processors 1712, 1714 (alsoreferred to as “cores”) that can execute instructions 1716contemporaneously. Although FIG. 17 shows multiple processors 1710, themachine 1700 may include a single processor 1710 with a single core, asingle processor 1710 with multiple cores (e.g., a multi-core processor1710), multiple processors 1712, 1714 with a single core, multipleprocessors 1712, 1714 with multiples cores, or any combination thereof.

The memory 1730 comprises a main memory 1732, a static memory 1734, anda storage unit 1736 accessible to the processors 1710 via the bus 1702,according to some embodiments. The storage unit 1736 can include amachine-readable medium 1738 on which are stored the instructions 1716embodying any one or more of the methodologies or functions describedherein. The instructions 1716 can also reside, completely or at leastpartially, within the main memory 1732, within the static memory 1734,within at least one of the processors 1710 (e.g., within the processor'scache memory), or any suitable combination thereof, during executionthereof by the machine 1700. Accordingly, in various embodiments, themain memory 1732, the static memory 1734, and the processors 1710 areconsidered machine-readable media 1738.

As used herein, the term “memory” refers to a machine-readable medium1738 able to store data temporarily or permanently and may be taken toinclude, but not be limited to, random-access memory (RAM), read-onlymemory (ROM), buffer memory, flash memory, and cache memory. While themachine-readable medium 1738 is shown, in an example embodiment, to be asingle medium, the term “machine-readable medium” should be taken toinclude a single medium or multiple media (e.g., a centralized ordistributed database, or associated caches and servers) able to storethe instructions 1716. The term “machine-readable medium” shall also betaken to include any medium, or combination of multiple media, that iscapable of storing instructions (e.g., instructions 1716) for executionby a machine (e.g., machine 1700), such that the instructions 1716, whenexecuted by one or more processors of the machine 1700 (e.g., processors1710), cause the machine 1700 to perform any one or more of themethodologies described herein. Accordingly, a “machine-readable medium”refers to a single storage apparatus or device, as well as “cloud-based”storage systems or storage networks that include multiple storageapparatus or devices. The term “machine-readable medium” shallaccordingly be taken to include, but not be limited to, one or more datarepositories in the form of a solid-state memory (e.g., flash memory),an optical medium, a magnetic medium, other non-volatile memory (e.g.,erasable programmable read-only memory (EPROM)), or any suitablecombination thereof. The term “machine-readable medium” specificallyexcludes non-statutory signals per se.

The I/O components 1750 include a wide variety of components to receiveinput, provide output, produce output, transmit information, exchangeinformation, capture measurements, and so on. In general, it will beappreciated that the I/O components 1750 can include many othercomponents that are not shown in FIG. 17. The I/O components 1750 aregrouped according to functionality merely for simplifying the followingdiscussion, and the grouping is in no way limiting. In various exampleembodiments, the I/O components 1750 include output components 1752 andinput components 1754. The output components 1752 include visualcomponents (e.g., a display such as a plasma display panel (PDP), alight emitting diode (LED) display, a liquid crystal display (LCD), aprojector, or a cathode ray tube (CRT)), acoustic components (e.g.,speakers), haptic components (e.g., a vibratory motor), other signalgenerators, and so forth. The input components 1754 include alphanumericinput components (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or other pointinginstruments), tactile input components (e.g., a physical button, a touchscreen that provides location and force of touches or touch gestures, orother tactile input components), audio input components (e.g., amicrophone), and the like.

In some further example embodiments, the I/O components 1750 includebiometric components 1756, motion components 1758, environmentalcomponents 1760, or position components 1762, among a wide array ofother components. For example, the biometric components 1756 includecomponents to detect expressions (e.g., hand expressions, facialexpressions, vocal expressions, body gestures, or eye tracking), measurebiosignals (e.g., blood pressure, heart rate, body temperature,perspiration, or brain waves), identify a person (e.g., voiceidentification, retinal identification, facial identification,fingerprint identification, or electroencephalogram basedidentification), and the like. The motion components 1758 includeacceleration sensor components (e.g., accelerometer), gravitation sensorcomponents, rotation sensor components (e.g., gyroscope), and so forth.The environmental components 1760 include, for example, illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometers that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensor components(e.g., machine olfaction detection sensors, gas detection sensors todetect concentrations of hazardous gases for safety or to measurepollutants in the atmosphere), or other components that may provideindications, measurements, or signals corresponding to a surroundingphysical environment. The position components 1762 include locationsensor components (e.g., a Global Positioning System (GPS) receivercomponent), altitude sensor components (e.g., altimeters or barometersthat detect air pressure from which altitude may be derived),orientation sensor components (e.g., magnetometers), and the like.

Communication can be implemented using a wide variety of technologies.The I/O components 1750 may include communication components 1764operable to couple the machine 1700 to a network 1780 or devices 1770via a coupling 1782 and a coupling 1772, respectively. For example, thecommunication components 1764 include a network interface component oranother suitable device to interface with the network 1780. In furtherexamples, communication components 1764 include wired communicationcomponents, wireless communication components, cellular communicationcomponents, near field communication (NFC) components, BLUETOOTH®components (e.g., BLUETOOTH® Low Energy), WI-FI® components, and othercommunication components to provide communication via other modalities.The devices 1770 may be another machine 1700 or any of a wide variety ofperipheral devices (e.g., a peripheral device coupled via a UniversalSerial Bus (USB)).

Moreover, in some embodiments, the communication components 1764 detectidentifiers or include components operable to detect identifiers. Forexample, the communication components 1764 include radio frequencyidentification (RFID) tag reader components, NFC smart tag detectioncomponents, optical reader components (e.g., an optical sensor to detecta one-dimensional bar codes such as a Universal Product Code (UPC) barcode, multi-dimensional bar codes such as a Quick Response (QR) code,Aztec Code, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code,Uniform Commercial Code Reduced Space Symbology (UCC RSS)-2D bar codes,and other optical codes), acoustic detection components (e.g.,microphones to identify tagged audio signals), or any suitablecombination thereof. In addition, a variety of information can bederived via the communication components 1764, such as location viaInternet Protocol (IP) geo-location, location via WI-FI® signaltriangulation, location via detecting a BLUETOOTH® or NFC beacon signalthat may indicate a particular location, and so forth.

In various example embodiments, one or more portions of the network 1780can be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), the Internet, a portion of the Internet, a portion of the publicswitched telephone network (PSTN), a plain old telephone service (POTS)network, a cellular telephone network, a wireless network, a WI-FI®network, another type of network, or a combination of two or more suchnetworks. For example, the network 1780 or a portion of the network 1780may include a wireless or cellular network, and the coupling 1782 may bea Code Division Multiple Access (CDMA) connection, a Global System forMobile communications (GSM) connection, or another type of cellular orwireless coupling. In this example, the coupling 1782 can implement anyof a variety of types of data transfer technology, such as SingleCarrier Radio Transmission Technology (1×RTT), Evolution-Data Optimized(EVDO) technology, General Packet Radio Service (GPRS) technology,Enhanced Data rates for GSM Evolution (EDGE) technology, thirdGeneration Partnership Project (3GPP) including 3G, fourth generationwireless (4G) networks, Universal Mobile Telecommunications System(UMTS), High Speed Packet Access (HSPA), Worldwide Interoperability forMicrowave Access (WiMAX), Long Term Evolution (LTE) standard, othersdefined by various standard-setting organizations, other long rangeprotocols, or other data transfer technology.

In example embodiments, the instructions 1716 are transmitted orreceived over the network 1780 using a transmission medium via a networkinterface device (e.g., a network interface component included in thecommunication components 1764) and utilizing any one of a number ofwell-known transfer protocols (e.g., Hypertext Transfer Protocol(HTTP)). Similarly, in other example embodiments, the instructions 1716are transmitted or received using a transmission medium via the coupling1772 (e.g., a peer-to-peer coupling) to the devices 1770. The term“transmission medium” shall be taken to include any intangible mediumthat is capable of storing, encoding, or carrying the instructions 1716for execution by the machine 1700, and includes digital or analogcommunications signals or other intangible media to facilitatecommunication of such software.

Furthermore, the machine-readable medium 1738 is non-transitory (inother words, not having any transitory signals) in that it does notembody a propagating signal. However, labeling the machine-readablemedium 1738 “non-transitory” should not be construed to mean that themedium is incapable of movement, the medium 1738 should be considered asbeing transportable from one physical location to another. Additionally,since the machine-readable medium 1738 is tangible, the medium 1738 maybe considered to be a machine-readable device.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Although an overview of the inventive subject matter has been describedwith reference to specific example embodiments, various modificationsand changes may be made to these embodiments without departing from thebroader scope of embodiments of the present disclosure

The embodiments illustrated herein are described in sufficient detail toenable those skilled in the art to practice the teachings disclosed.Other embodiments may be used and derived therefrom, such thatstructural and logical substitutions and changes may be made withoutdeparting from the scope of this disclosure. The Detailed Description,therefore, is not to be taken in a limiting sense, and the scope ofvarious embodiments is defined only by the appended claims, along withthe full range of equivalents to which such claims are entitled.

As used herein, the term “or” may be construed in either an inclusive orexclusive sense. Moreover, plural instances may be provided forresources, operations, or structures described herein as a singleinstance. Additionally, boundaries between various resources,operations, modules, engines, and data stores are somewhat arbitrary,and particular operations are illustrated in a context of specificillustrative configurations. Other allocations of functionality areenvisioned and may fall within a scope of various embodiments of thepresent disclosure. In general, structures and functionality presentedas separate resources in the example configurations may be implementedas a combined structure or resource. Similarly, structures andfunctionality presented as a single resource may be implemented asseparate resources. These and other variations, modifications,additions, and improvements fall within a scope of embodiments of thepresent disclosure as represented by the appended claims. Thespecification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense.

What is claimed is:
 1. A method comprising: receiving, at a servercomputer, a query from a user via a computing device; accessing, by theserver computer, a plurality of modeled queries, each modeled query ofthe plurality of modeled queries associated with one or more predefinedintents in a list of predefined intents to be used to respond to aquery; comparing, by the server computer, the query received from theuser to each modeled query of the plurality of modeled queries todetermine that the query received from the user does not correspond to amodeled query in the plurality of modeled queries; based on determiningthat the query received from the user does not correspond to a modeledquery in the plurality of modeled queries, providing a message to bepresented on a display of the computing device indicating that the queryentered is not understood and generating, by the server computer,recommended queries to be presented to the user for selection byperforming operations comprising: processing, by the server computer,language in the query to identify a plurality of elements of the query;comparing, by the server computer, the plurality of elements of thequery to each predefined intent in the list of predefined intents to beused to respond to a query, to generate a score for each predefinedintent in the list of predefined intents based on how frequently eachelement of the query appears in each predefined intent of list ofpredefined intents; generating, by the server computer, a total scorefor each intent by adding a learned score corresponding to the intent tothe generated score and subtracting a penalty score corresponding to theintent from the generated score; selecting, by the server computer, asubset of predefined intents of the list of predefined intents based onthe total score for each predefined intent in the list of predefinedintents being above a predefined threshold score, each predefined intentof the subset of predefined intents associated with a modeled query; andproviding, to the computing device, a recommended query comprising themodeled query corresponding to each predefined intent of the selectedsubset of predefined intents of the list of predefined intents to bepresented on the display of the computing device for selection by theuser to clarify the query received from the user.
 2. The method of claim1, further comprising: ranking the list of predefined intents by thetotal score generated for each intent; wherein selecting the subset ofpredefined intents of the list of predefined intents based on the totalscore for each predefined intent in the list of predefined intentcomprises determining a specified number of top scoring intents based onthe ranking; and selecting the top scoring intents for the subset ofpredefined intents of the list of predefined intents.
 3. The method ofclaim 1, further comprising: receiving a selection of one recommendedquery corresponding to one intent of the selected subset of predefinedintents; calculating a learned score for the one intent based onreceiving the selection of the one recommended query corresponding tothe one intent; and storing the calculated learned score for the oneintent.
 4. The method of claim 3, wherein the query is a first query,and wherein the method further comprises: receiving a second query froma user via a computing device; processing language in the second queryto identify a plurality of elements associated with the second query;comparing the plurality of elements associated with the second query toeach intent in the list of predefined intents to generate a second scorefor each intent in the list of predefined intents; selecting the oneintent based on the learned score for the one intent; generating aresponse to the second query, based on the selected one intent.
 5. Themethod of claim 3, further comprising: determining that a particularintent is associated with a high learned score for a plurality of usersin a single organization; generating a learned score for the particularintent and associating it with the single organization.
 6. The method ofclaim 3, further comprising: determining that a particular intent isassociated with a high learned score for a plurality of users acrossmultiple organizations in a system; creating a default score for theparticular intent in the system.
 7. The method of claim 1, furthercomprising: receiving an indication from the computing device that atleast one intent of the subset of predefined intents of the list ofpredefined intents is not accurate for the intent of the query;calculating a penalty score for the at least one intent of subset ofpredefined intents of the list of predefined intents; and storing thepenalty score for the at least one intent and associating it with theuser.
 8. The method of claim 1, further comprising: receiving aselection of one recommended query corresponding to one intent of theselected subset of predefined intents of the list of predefined intents;retrieving data related to the one recommended query; generating aresponse based on the retrieved data; and sending the response to theone recommended query to the computing device, wherein the response isdisplayed to a user.
 9. The method of claim 8, wherein the dataretrieved related to the one recommended query comprises differentformats of data from different data sources, and wherein generating aresponse based on the retrieved data comprises transforming theretrieved data into a format for a response to the query by transformingthe different formats of data from the different data sources into onedata format.
 10. A server computer comprising: at least one processor;and a computer-readable medium coupled with the at least one processor,the computer-readable medium comprising instructions stored thereon thatare executable by the at least one processor to cause the servercomputer to perform operations comprising: receiving a query from a uservia a computing device; accessing a plurality of modeled queries, eachmodeled query of the plurality of modeled queries associated with one ormore predefined intents in a list of predefined intents to be used torespond to a query; comparing the query received from the user to eachmodeled query of the plurality of modeled queries to determine that thequery received from the user does not correspond to a modeled query inthe plurality of modeled queries; based on determining that the queryreceived from the user does not correspond to a modeled query in theplurality of modeled queries, providing a message to be presented on adisplay of the computing device indicating that the query entered is notunderstood and generating recommended queries to be presented to theuser for selection by performing operations comprising: processinglanguage in the query to identify a plurality of elements of the query;comparing the plurality of elements of the query to each predefinedintent in the list of predefined intents to be used to respond to aquery, to generate a score for each predefined intent in the list ofpredefined intents based on how frequently each element of the queryappears in each predefined intent of list of predefined intents;generating a total score for each intent by adding a learned scorecorresponding to the intent to the generated score and subtracting apenalty score corresponding to the intent from the generated score;selecting a subset of predefined intents of the list of predefinedintents based on the total score for each predefined intent in the listof predefined intents being above a predefined threshold score, eachpredefined intent of the subset of predefined intents associated with amodeled query; and providing, to the computing device, a recommendedquery comprising the modeled query corresponding to each predefinedintent of the selected subset of predefined intents of the list ofpredefined intents to be presented on the display of the computingdevice for selection by the user to clarify the query received from theuser.
 11. The server computer of claim 10, the operations furthercomprising: receiving a selection of one recommended query correspondingto one intent of the selected subset of predefined intents of the listof predefined intents; calculating a learned score for the one intentbased on receiving the selection of the one recommended querycorresponding to the one intent; and storing the calculated learnedscore for the one intent.
 12. The server computer of claim 11, whereinthe query is a first query, and wherein the operations furthercomprises: receiving a second query from a user via a computing device;processing language in the second query to identify a plurality ofelements associated with the second query; comparing the plurality ofelements associated with the second query to each intent in the list ofpredefined intents to generate a second score for each intent in thelist of predefined intents; selecting the one intent based on thelearned score for the one intent; generating a response to the secondquery, based on the selected one intent.
 13. The server computer ofclaim 11, the operations further comprising: determining that aparticular intent is associated with a high learned score for aplurality of users in a single organization; generating a learned scorefor the particular intent and associating it with the singleorganization.
 14. The server computer of claim 11, the operationsfurther comprising: determining that a particular intent is associatedwith a high learned score for a plurality of users across multipleorganizations in a system; creating a default score for the particularintent in the system.
 15. The server computer of claim 10, theoperations further comprising: receiving an indication from thecomputing device that at least one intent of the subset of predefinedintents of the list of predefined intents is not accurate for the intentof the query; calculating a penalty score for the at least one intent ofthe subset of predefined intents of the list of predefined intents; andstoring the penalty score for the at least one intent and associating itwith the user.
 16. The server computer of claim 10, the operationsfurther comprising: receiving a selection of one recommended querycorresponding to one intent of the selected subset of predefined intentsof the list of predefined intents; retrieving data related to the onerecommended query; generating a response based on the retrieved data;and sending the response to the one recommended query to the computingdevice, wherein the response is displayed to a user.
 17. The servercomputer of claim 16, wherein the data retrieved related to the onerecommended query comprises different formats of data from differentdata sources, and wherein generating a response based on the retrieveddata comprises transforming the retrieved data into a format for aresponse to the query by transforming the different formats of data fromthe different data sources into one data format.
 18. A non-transitorycomputer-readable medium comprising instructions stored thereon that areexecutable by at least one processor to cause a computing device toperform operations comprising: receiving a query from a user via acomputing device; accessing a plurality of modeled queries, each modeledquery of the plurality of modeled queries associated with one or morepredefined intents in a list of predefined intents to be used to respondto a query; comparing the query received from the user to each modeledquery of the plurality of modeled queries to determine that the queryreceived from the user does not correspond to a modeled query in theplurality of modeled queries; based on determining that the queryreceived from the user does not correspond to a modeled query in theplurality of modeled queries, providing a message to be presented on adisplay of the computing device indicating that the query entered is notunderstood and generating recommended queries to be presented to theuser for selection by performing operations comprising: processinglanguage in the query to identify a plurality of elements of the query;comparing the plurality of elements of the query to each predefinedintent in the list of predefined intents to be used to respond to aquery, to generate a score for each predefined intent in the list ofpredefined intents based on how frequently each element of the queryappears in each predefined intent of list of predefined intents;generating a total score for each intent by adding a learned scorecorresponding to the intent to the generated score and subtracting apenalty score corresponding to the intent from the generated score;selecting a subset of predefined intents of the list of predefinedintents based on the total score for each predefined intent in the listof predefined intents being above a predefined threshold score, eachpredefined intent of the subset of predefined intents associated with amodeled query; and providing, to the computing device, a recommendedquery comprising the modeled query corresponding to each predefinedintent of the selected subset of predefined intents of the list ofpredefined intents to be presented on the display of the computingdevice for selection by the user to clarify the query received from theuser.
 19. The method of claim 1, wherein at least one intent of the listof predefined intents comprises more than one recommended querycorresponding the at least one intent.
 20. The method of claim 1,wherein the query is a first query, and the method further comprises:receiving a second query via the computing device; determining that thesecond query is similar to the first query; generating a response to thesecond query based on the selected one recommended query correspondingto one intent of the selected subset of predefined intents of the listof predefined intents; and sending the generated response to thecomputing device to be displayed to the user.